ExtraSensory: We use the publicly-available ExtraSensory [1]} human activity recognition dataset, which contains smartphone sensor data collected across one week while participants labeled which actions they performed and when.
These data were collected in the wild, so participants were left to their own devices for the duration of the study with no prescribed behavior.
Using these data, we simulate a listening probe on a smartphone's accelerometer data, which consist of three variables corresponding to the X, Y, and Z coordinates.
A listening probe saves a phone's battery by collecting data only when certain measurements are taken, naturally creating irregular time series.
For this dataset, we measure the norm of the 3-dimensional accelerometer data, only taking measurements associated with changes in the norm over 0.001.
Here we consider the popular tasks of detecting Walking and Running, classifying whether or not a person performs an activity within a window.
Since activity records are often incomparable between people, we use the records from the person who walked or ran the most breaking their series into 100-minute long windows.
This creates two independent datasets, one per user, which is common for human activity recognition.
We balance each dataset, resulting in 2636 time series with an average of 90 observation per series for Walking and 3000 time series with on average 100 observations for Running.
Deeper, extended discussion of this dataset is available in a concurrent submission.
In general, down-sampling human activity recognition data is common in the irregular time series literature [2]}, [3]}, and simulating a listening probe adds a key new ingredient: non-random down-sampling.
